Agentic Call Quality Intelligence

An end-to-end AI workflow that transcribes, evaluates, and transforms customer calls into actionable quality insights.

Inspiration

Call center quality assurance is still largely a manual process. Due to the high volume of customer interactions, supervisors can only review a small percentage of recorded calls. This not only consumes significant time but also creates inconsistencies, as evaluation results may vary between reviewers and important service issues can be overlooked.

We wanted to build a solution that enables every recorded conversation to be evaluated consistently, objectively, and at scale. By leveraging AI Agents and UiPath Agentic Automation, supervisors can spend less time reviewing calls manually and more time coaching agents, improving customer experience, and resolving high-priority issues.

What it does

Agentic Call Quality Intelligence automates the call center quality assurance process by transforming recorded customer conversations into actionable insights.

The solution automatically transcribes recorded calls, evaluates agent performance against predefined QA criteria, analyzes customer sentiment, detects compliance and service quality risks, and generates explainable quality scores with supporting observations.

Instead of manually reviewing only a small sample of calls, supervisors receive prioritized insights, coaching opportunities, and dashboards that help them focus on the conversations that matter most.

How we built it

We built the solution using UiPath Agentic Automation together with AI-powered speech and language processing technologies.

Our workflow automatically:

-Ingests recorded customer calls. -Converts speech into structured transcripts. -Uses AI Agents to evaluate conversations against predefined QA criteria. -Detects customer sentiment, compliance risks, and service quality issues. -Generates quality scores, observations, and coaching recommendations. -Stores evaluation results for dashboards and reporting. -Routes high-risk or low-scoring calls to supervisors for further review.

UiPath orchestrates the entire workflow, enabling scalable, automated, and consistent call quality evaluation.

Challenges we ran into

One of our biggest challenges was designing and delivering a complete end-to-end solution within the three-week hackathon timeline.

This project was inspired by a real business requirement to improve call center quality assurance, so reliability and consistency were just as important as functionality. We needed to integrate speech-to-text, AI-powered evaluation, sentiment analysis, and workflow automation into a seamless process while ensuring the results remained explainable and aligned with QA standards.

Accomplishments that we're proud of

We're proud to have delivered a working proof of concept that addresses a real operational challenge in call center quality assurance.

Within just three weeks, we built an end-to-end solution that automatically transforms recorded conversations into structured quality insights, including AI-powered evaluation, sentiment analysis, risk detection, and actionable dashboards. The project demonstrates how Agentic AI can help supervisors focus on coaching and continuous improvement instead of spending hours manually reviewing calls.

What we learned

This project gave us valuable experience in applying Agentic AI to solve a real business problem rather than building a technology demo.

We learned how to orchestrate multiple AI capabilities with UiPath Agentic Automation, design explainable evaluation workflows, and balance automation with the transparency needed for quality assurance and operational decision-making.

What's next for Agentic Call Quality Intelligence

Our next step is to validate the solution through a pilot deployment within a real QA workflow, comparing AI-generated evaluations with the current manual review process. Based on operational feedback, we will continue refining the evaluation criteria, prompts, and transcript confidence thresholds to improve accuracy and consistency.

As the solution evolves, we plan to expand support for additional call scenarios and service teams while enhancing the quality insights available to supervisors. By combining structured evaluation data with natural language exploration, we envision enabling supervisors to identify recurring issues, uncover coaching opportunities, and make faster, data-driven decisions to continuously improve customer service.